Data Scientist-Multitasking Job

Firms use data science aggressively to be a market leader. Data is streaming in from different sources like web, social media, customer reviews, internal databases, and governmental datasets. But just having those data stored will not help firms in any way, to utilize the data one needs to analyze it. Analyzing data is not an easy job as the trends are hidden.

The data science industry is earning revenues from all industries domestic and international both. Revenue of $1.27 billion is earned in the last year only and it is predicted to touch $20 billion by 2025. This sudden growth is because big data is proving to be of great value to the business. Some of the uses are:

Helping understand market demand.

Helps in the innovation of new products and services.

Helps in customer retention and satisfaction.

Helps in communicating the brand to the customers.

Helps in digital and social media marketing.

Helps in real-time experimentation and keeps a check on business performance.

ROLES OF DATA SCIENTISTS:

Data scientists are data wranglers who search for meaning in the data collected. A data professional has many roles in their data to day activities. As the entire data process is a pipeline of many steps linked together, a data scientist might do them all together or separate experts are appointed to complete the process. Some of the roles performed by them are:

Conduct research and frame a problem that is market relevant.

Collect data from various internal and external sources like web, internal databases, datasets available on the internet or customer reviews on social media platforms.

Clean and scourge the data from all the inconsistencies like gaps and wrongly entered figures, time zone differences, etc.

Explore the data from all the directions to find any kind of behavioral patterns or trends hidden in it. For this many tools are used which are programmed for exploratory data analysis.

Use statistical and mathematical models and tools to deep learn the data, and prepare it for predictive decision making.

Build new algorithms which are also called machine learning, where data is used for automating the work.

Communicate the inferences learned in using data visualization tools and present in a way that can be understood by management.

Proper understanding will lead to actionable decision making and finding solutions that can be applied in a practical way.

Different companies have different tasks lined up for their data analysis, but most of the activities remain similar.

SKILLS OF A DATA SCIENTIST:

Data scientists need to have several skills up their sleeves. But the most important of them is to have a curious mind and an analytical mindset. Searching for a question and then like detective sniffing out answers from a massive amount of data is no joke. Core traits like patience, curiosity, and contextual understanding can help one become successful. The rest of the knowledge is technical and that can be learned and practiced. Some of the skills needed are:

Mathematics, statistics, and probability.

Programming and coding.

Cloud computing (Amazon S3)

Machine learning and modeling

Database management.

Tools like Python, Apache Spark, and Flink, Hadoop, Pig & Hive.

SQL, Java, C/C++

Industry knowledge.

Presentation and communication skills.

Decision-making skills.

The industry of every size and influence demands these skills from their experts and to be a successful data scientist these are mandatory requirements.